Diverging effects of global change on future invasion risks of <i>Agama picticauda</i> between invaded regions: same problem, different solutions
Bibliographic record
Abstract
Abstract Predicting biological invasions is challenging because multiple factors can act in contrasting directions and exert heterogeneous effects across space. Nevertheless, modelling approaches provide valuable tools to anticipate the potential spread of invasive alien species and to support mitigation strategies. With an Ecological Niche Modelling approach, we predicted the invasion risks of Peters’s Rock Agama Agama picticauda , a species that is spreading globally in non-forested areas through freight transport and un-/intentional releases from the pet trade. The potential establishment of the species in new areas is of concern for multiple endemic species throughout the world. We quantified the effects of climate, anthropogenic activity and forest cover on invasion risk. We used verified records from the native and non-native range and accounted for the latest methodological recommendations. We predicted how invasion risk will vary in the future (2070) using projections from two scenarios (SSP2 and SSP5). We predict that invasion risks will vary in diverging directions, depending on the region. The risk will increase in human-populated regions and on small islands but will decrease in Florida. We recommend increasing surveillance in vehicular transportation of material especially within the Comoros and the Mascarenes archipelagos. Since many introductions are related to the pet trade in Florida, we recommend stronger legal regulations and the promotion of public awareness. Promoting tree cover may be locally beneficial to prevent establishment of A. picticauda . The effect of climate change, land use change and human activities may differ between and within both, the native and the invaded regions. Abstract Figure
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".